The massive response of the scientific community to the COVID-19 pandemic has produced an unprecedented amount of research and related publications. For example, the Cord-19 corpus [1]} currently includes more than five hundred thousand published peer reviewed and pre-peer reviewed articles. This huge volume of COVID-related works and the fast emergence of new research branches far exceeds the human capability to meaningfully organize and explore such material. This makes it impossible, even to a specialist, to map, explore and summarise such a massive corpus of documents without the help of automated tools that can extract and classify useful semantic information from unstructured texts. A key issue related to the analysis of large scientific corpora consists in identifying topics that may be spread across many disciplines or different research branches.
